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    "# 基于深度学习的个性化召回算法item2vec\n",
    "\n",
    "## 背景\n",
    "\n",
    "- item2item的推荐方式效果显著\n",
    "\n",
    "- 神经网络模型的特征抽象能力\n",
    "\n",
    "- 算法依据论文：ITEM2VEC:NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING\n",
    "\n",
    "## 物理意义\n",
    "\n",
    "- 将用户的行为序列转化为item组成的句子\n",
    "\n",
    "- 模仿word2vec训练word embedding的过程，将item进行embedding\n",
    "\n",
    "## 缺陷\n",
    "\n",
    "- 用户的行为序列时序性缺失\n",
    "\n",
    "- 用户行为序列中的item强度是无区分性的\n",
    "\n",
    "## 主流程\n",
    "\n",
    "- 从log中抽取用户行为序列\n",
    "\n",
    "- 将行为序列当成语料，训练word2vec，得到item embedding\n",
    "\n",
    "- 得到item sim关系用于推荐"
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